Large Scale System Architecture and Evolution Questions

Design and evolution of architectures to support massive user bases large data volumes and very high request rates. Topics include global distribution strategies such as geographic partitioning and multi region replication; high throughput low latency design choices including careful partitioning efficient data pipelines and edge caching; storage and data lifecycle strategies for petabyte scale including tiered storage and efficient compaction; federation and aggregation patterns for global services; migration strategies for rewarding systems and rolling upgrades; and operational concerns for large fleets including monitoring alerting incident response and cost management. Interviewers assess the candidate on ability to reason about long term maintainability operational scaling and trade offs required to run systems at extreme scale.

HardTechnical

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A distributed caching layer is experiencing cache stampedes under burst traffic, degrading origin services. As PM propose architectural and product-level mitigations (e.g., request coalescing, jittered TTL, backpressure, graceful degradation), a rollout strategy, and experiments to validate improvements.

HardSystem Design

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Design a cross-region disaster recovery strategy for a database-backed service that requires RTO < 10 minutes and RPO < 1 minute for critical workflows, while analytics can tolerate 24-hour RPO. Explain replication topology, failover automation, data integrity checks, and how you'd notify and support customers during failover.

EasyTechnical

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List the core monitoring and alerting metrics you would include on an initial SLO dashboard for a high-throughput messaging service that handles 200k messages/sec. For each metric include why it matters and give an example alert threshold or SLI target (e.g., p99 latency, error rate).

EasyTechnical

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Explain trade-offs between pushing computation to the edge (edge computing) versus centralizing compute in core regions for a latency-sensitive real-time multiplayer game. For each option, list the product metrics (latency, cost, consistency) and experiments you would run to decide the approach.

EasyTechnical

0 practiced

Define a latency budget for a core user flow (search) and describe a simple process for setting, owning, and reviewing the budget including inputs (client behavior, market expectations), an owner role, and review cadence. How would you enforce the budget during feature planning?

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